Internet, mobile communications, navigation, online gaming, sensing technologies and large scale computing infrastructures are producing large amounts of data sets every day. Big Data is data that is beyond the processing capacity of conventional database systems and analyzing capacity of traditional analyzing methods due to its large volume and fast moving and growing speed. More companies now rely on Big Data to make real-time decisions to solve various problems. Current methods involve utilizing a lot of computational resources, which are very costly, yet still may not satisfy the needs of real-time decision making based on the newest information, especially in the financial industry. How to efficiently, promptly and cost-effectively process and analyze Big Data presents a difficult challenge to data analysts and computer scientists.
Processing Big Data may include performing calculations on multiple data elements. When performing statistical calculations on Big Data, the number of data elements to be accessed may be quite large. For example, when calculating an autocorrelation a (potentially large) number of data elements may need to be accessed.
Further, some statistical calculations are recalculated after some data changes in a Big Data set. Thus, the (potentially large) number of data elements may be repeatedly accessed. For example, it may be that an autocorrelation function is calculated for a computation window that includes the last n data elements added to a Big Data set that is stored in storage media. As such, every time two data elements are accessed or received, one of the accessed or received elements is added to the computation window and the other data element is removed from the computation window. The n data elements in the computation window are then accessed to recalculate the autocorrelation function.
As such, each data change in the computation window may only change a small portion of the computation window. Using all data elements in the computation window to recalculate the autocorrelation function involves redundant data access and computation, and thus is time consuming and is an inefficient use of resources.
Depending on necessity, the computation window size n may be extremely large, so the data elements in a computation window may be distributed over a cloud comprising hundreds of thousands of computing devices. Re-performing an autocorrelation function calculation in traditional ways on Big Data after some data changes inefficiently uses time and computing resources.